Exploring Quantum Computing for Fashion Retail Inventory Optimisation

Executive Summary

Global fashion retail is under intense pressure from slowing growth and new sustainability mandates. McKinsey’s State of Fashion 2025 reports that only 20% of fashion executives expect consumer sentiment to improve in 2025, whilst 39% expect conditions to worsen [1]. Meanwhile, the industry carried an estimated 2.5 to 5 billion excess items in 2023—worth roughly £55-110 billion in sales value (based on $70-140 billion) [2]. These facts underscore the need for continuous, intelligent inventory management rather than slow, seasonal planning.

Traditional supply-chain tools struggle to react fast enough to real-time data (such as 15-minute store telemetry) or volatile trends. Quantum computing, applied via a micro-optimisation architecture, represents a promising path forward. By decomposing the global network into thousands of small, parallel optimisation tasks (each with 50-200 decision variables), a hybrid quantum-classical system could potentially run continuous, localised optimisations within the reach of today’s hardware. Initial financial modelling suggests compelling potential: a mean NPV of £1.02 billion over five years for large Tier 1 retailers (£20-25B revenue, 4,000+ stores)—though real-world results would naturally vary based on implementation quality and organisational factors.

Key Industry Metrics:

• Consumer Outlook: Only ~20% of fashion leaders predict better consumer sentiment (vs 39% predicting worse) [1]
• Excess Inventory: 2.5-5 billion unsold garments (£55-110 billion of inventory) in 2023, reflecting major forecasting and allocation gaps [2]
• Warehouse Automation: Modern automated warehouses can achieve ~99% inventory accuracy (a 76% gain) and routinely ship orders in one day (40% faster) [3]—highlighting node-level gains that need network-wide orchestration
• Size Misalignment: Inaccurate stock buying across sizes can result in profit loss of up to 20% on average [2]

This analysis explores why fashion retail may be uniquely positioned for quantum-accelerated inventory optimisation. The opportunity is intriguing: in a low-growth, high-pressure industry, even modest efficiency gains could translate to competitive advantage. What makes this particularly exciting is that the micro-optimisation approach could make quantum computing practical today for specific, well-defined inventory problems—moving quantum from research curiosity to operational tool.

The Current State: Fashion Retail’s Inventory Challenge

Fashion retail is uniquely complex, and classical methods struggle to keep up. Key factors include:

Network Density: Large global retailers manage thousands of stores, numerous distribution centres (fulfillment centres), and many country- or region-specific online channels. Each node (store or warehouse) sees distinct demand patterns. For example, a retailer might have 4,000+ stores and several dozen omni-channel warehouses, each requiring its own inventory balancing. This sprawling network—with millions of possible allocations—makes full-network optimisation extremely complex.

Product Complexity: Retail assortments feature millions of product variants (style, colour, size, collection, etc.). Localised trends and demographics drive differences in demand and size distribution. Traditional “one-size-fits-all” allocations lead to mismatches: some stores over-stock slow-selling sizes whilst others stock out of fast movers. Inaccurate stock sizing can erode about 20% of profit on average [2]. Moreover, fashion now operates on a collection-based, fast-cycle model rather than classic seasons, compressing decision timelines. Meanwhile, social-media-driven “viral” trends cause demand spikes. For instance, trending styles on TikTok saw search volume swings up to 300% within a year [2], underscoring the huge volatility retailers face.

Velocity Requirements: Modern retail generates real-time data: smart shelves report inventory changes every 15 minutes, and warehouses log pick confirmations instantly. In such an environment, waiting for a nightly (or weekly) batch optimisation run introduces costly lag. Automation has improved node efficiency—automated warehouses now reach ~99% inventory accuracy and ship in one day (40% faster) [3]—but without intelligent coordination, these gains remain siloed. In short, supply and demand can change in minutes, yet planning often remains on a 24-hour cycle. Any delay leads to missed matches of stock to demand.

These challenges—extensive networks, massive SKU complexity, and rapid data flows—mean that legacy inventory tools fall short. Without continuous, multi-echelon optimisation, retailers face chronic excess stock and lost sales.

The £55-110 Billion Efficiency Opportunity

The magnitude of this problem is staggering. Recent analysis from McKinsey’s State of Fashion 2025 found that fashion brands produced an estimated 2.5-5 billion items of excess stock in 2023, equivalent to $70-140 billion in potential sales value (approximately £55-110 billion) [2]. This unsold inventory ties up working capital and often ends up heavily discounted or scrapped.

What makes this particularly interesting for quantum computing is that the drivers of this inefficiency are fundamentally optimisation challenges:

Demand Volatility: Consumer demand in fashion is highly unpredictable. Weather swings, social media trends, and competitive moves can cause sudden surges or drops. (For example, one viral style can see interest jump several hundred percent virtually overnight [2].) Classical statistical forecasts—even those augmented by machine learning—struggle with such “black swan” shifts. By the time manual planners react, the opportunity is gone.

Size-Curve Misalignment: The ideal size mix for a collection varies by store. Using historical size ratios leads to systemic imbalances: too many large sizes in one region and too few small sizes in another. Business of Fashion reports that out-of-stock sizes are the top shopper complaint and that mis-buying across sizes costs brands up to ~20% of profit on average [2]. In-season collection launches exacerbate this issue, requiring dynamic reallocation that traditional methods rarely achieve.

Clearance Timing: Deciding when to markdown excess stock is complex. Too early, and full-price potential is sacrificed; too late, and inventory loses value. With continuous releases of new collections, clearance becomes a rolling problem, not just a biannual event.

Industry studies suggest significant upside potential from smarter planning. BCG research shows that advanced planning (including AI and prescriptive analytics) can help reduce inventory by 15-30% whilst raising revenue by 2-4% through improved availability [4]. Given the fashion industry’s baseline inefficiency, achieving even the lower end of those gains would represent tens of billions in value. This £55-110 billion excess-stock problem could represent one of retail’s most promising opportunities for quantum optimisation—if the technology can be made practical for real-world deployment.

Why Traditional Approaches Fall Short

Conventional inventory systems rely on simplifications that are no longer adequate:

Overnight Batch Processing: Nearly all retailers run replenishment and allocation models in a nightly batch. This creates an inherent 24-48 hour data lag. In a market where local demand can shift hourly, such latency wastes opportunities. A stockout at 10:00am cannot wait until the next day to be addressed.

Problem Aggregation: To keep computations feasible, classical models often aggregate SKUs into broad categories (e.g., “tops” vs individual styles) or regions instead of individual stores. They may also ignore certain constraints (like transport bottlenecks). The solutions from these simplified problems, whilst optimal on paper, often misalign when deployed on the real, granular network.

Limited Scenario Scope: Traditional optimisers can test perhaps a few dozen forecast or disruption scenarios. Quantum-enhanced approaches, by contrast, can implicitly consider exponentially many possibilities in parallel, surfacing options that classical enumeration would miss.

In short, today’s planning systems can’t fully exploit real-time local signals. They lack the computational power to coordinate millions of inventory decisions continuously. This gap sets the stage for a new paradigm.

Strategic Architecture: Continuous Micro-Optimisation

The Paradigm Shift from Batch to Continuous

The promise of quantum inventory optimisation lies in enabling continuous, event-driven decision-making rather than static overnight runs. In practice, this could mean inventory decisions (replenishment, transfers, markdowns) being evaluated on-the-fly in response to incoming data:

• A store reports an unexpected demand spike (e.g., viral trend)
• The system immediately triggers a localised re-optimisation involving nearby stock
• If another store or warehouse has excess of the same item, a transfer could be initiated within minutes, rather than waiting for tomorrow

What makes this particularly exciting is the decomposition approach: breaking the global problem into many small, simultaneous optimisations. Potential domains include:

Store-Cluster Rebalancing: Breaking the network into 200-300 concurrent subproblems, each covering a small geographic cluster of 10-20 nearby stores. Each subproblem would have ~50-120 binary decisions (e.g., transfer item X to store Y or not) and run continuously (e.g., hourly). Such problem sizes sit comfortably within the capability of current quantum annealers.

Warehouse Wave Planning: Each fulfillment centre could optimise its picking and shipping waves every 15-30 minutes as orders accumulate. These optimisations (~80-150 variables each) would schedule which orders to pick or hold to minimise delays and costs.

Cross-Warehouse Routing: Deciding dynamically which warehouse (or store) should fulfil which order involves ~40-100 decision variables, considering inventory levels, transport costs, and service guarantees. The system could solve 50-100 of these problems in parallel as conditions change.

Demand-Shock Response: When sensors detect a sudden local trend (e.g., weather event or social buzz), 10-50 targeted micro-optimisations (60-180 variables each) could rebalance nearby supply in real time.

The aggregate network involves billions of potential variables, but no single quantum problem needs to be large—typically under 200 variables. This makes the approach potentially feasible on today’s hardware. For example, D-Wave’s current Advantage quantum annealer (5,000+ qubits) can solve QUBO problems with hundreds of binary variables in seconds. This suggests the quantum-enabled system could rapidly solve each micro-optimisation, with results then stitched together into a coherent global plan.

QUBO Formulation and Quantum Suitability

Quantum solvers work naturally with Quadratic Unconstrained Binary Optimisation (QUBO) problems of the form minimise: x^TQx with binary variables. Inventory decisions fit this mould:

Binary Choices: Examples include “ship SKU A from warehouse 1 to store 5” (yes=1/no=0) or “apply markdown on product B in region C” (yes/no).

Quadratic Interactions: Costs or benefits often depend on pairs of decisions. For instance, transferring two items together to the same store might yield a cross-selling bonus (positive correlation), whilst depleting one warehouse entirely creates a failure risk (interaction penalty). These effects are encoded as quadratic terms in Q.

Research from the Quantum Economic Development Consortium highlights that many logistics and inventory use cases—routing, allocation, scheduling—are inherently QUBO-compatible. By expressing business constraints (like capacity, service levels, substitution) as penalty terms, each micro-optimisation becomes a QUBO instance. Modern quantum hardware (annealers or gate-based QAOA solvers) can then seek high-quality solutions for these instances in real time. The micro-optimisation framework ensures each QUBO stays small enough for present devices, whilst the aggregate of thousands of solved instances yields global intelligence.

Hybrid Classical-Quantum Architecture

A practical deployment would likely use a hybrid stack, blending classical, quantum-inspired, and true quantum solvers:

Classical Pre-Processing: Incoming data (inventory levels, orders, forecasts) would be cleaned and checked. The system would partition the problem into the micro-instances described above and estimate their sizes and priorities.

Quantum-Inspired Classical Solving (~90% of volume): Most routine micro-optimisations could be handled by classical algorithms that mimic quantum annealing (e.g., digital annealers, GPU-accelerated heuristics). These can solve problems up to ~100 variables extremely quickly, providing a baseline solution layer at low cost.

True Quantum Acceleration (~10% of volume): The most complex, high-impact instances (typically 100-200 variables) could be sent to actual quantum hardware. This may be a D-Wave annealer or gate-based QPU (IBM, IonQ, Quantinuum) accessed via cloud platforms. These quantum runs could potentially find better solutions or do so faster for challenging cases, raising the overall solution quality. Over time, as hardware improves, more instances could be moved to quantum.

Classical Post-Processing: Each quantum or classical solution would be validated and repaired if it violates a constraint (via fast local search). Business rules (e.g., “do not stock low-turn SKU at small store”) would be enforced. Finally, solutions from all micro-problems would be combined into executable recommendations, checking for cross-problem coherence.

What’s encouraging is that this hybrid flow appears practical today. At the same time, quantum-inspired solvers (e.g., Fujitsu’s Digital Annealer, NVIDIA’s GPUs) can sweep through thousands of small problems cheaply. This means an organisation could potentially capture immediate value today, whilst retaining a path to leverage future quantum speedups as the technology matures.

Integration with Existing Infrastructure

This solution layer sits atop retailers’ current systems via defined interfaces:

Data Ingestion: Real-time feeds are required from store telemetry (15-minute inventory snapshots), warehouse management systems (order picks, capacity), eCommerce platforms (order flows, online demand signals), and enterprise planning systems (forecasts, assortments, constraints). A robust data pipeline consolidates this streaming data.

Solver Orchestration: A centralised engine formulates QUBO problems from business decisions, decomposes them into micro-instances, and routes each to the appropriate solver. It manages hundreds of concurrent optimisations, choosing classical or quantum resources based on problem size.

Execution & Feedback: Solutions are either auto-executed (for routine tasks like transfers or reserves) or presented via dashboards for planners (for strategic choices like markdown timing). The system continuously monitors outcomes and feeds back performance data into the next optimisation cycle.

Deployment can be phased: initial pilots might run non-critical optimisations in manual-review mode to build confidence. Over time, high-confidence, high-frequency decisions (e.g., store replenishment transfers) can be automated end-to-end. Throughout, legacy systems (ERP, WMS, replenishment engines) continue operating—the quantum layer augments them with superior optimisation, not replaces them wholesale.

Financial Analysis: Exploring the Value Potential

Monte Carlo Simulation Methodology

To explore the potential financial case, the analysis models a representative large retailer over 5 years with uncertainty. Key assumptions:

Investment Profile: £80M initial CAPEX (development, integration, pilot) and £12M/year OPEX (cloud solver fees, data platform, personnel). This covers the hybrid quantum-classical stack and change management programme.

Baseline Costs: Annual markdown costs of ~£450M (reflecting current clearance inefficiencies) and inventory carrying costs of ~£3.5B (tied-up capital, warehousing, obsolescence) for a £20-25B revenue Tier 1 retailer.

Controllable Optimisation Scope: Not all inventory costs are addressable through optimisation algorithms. The model conservatively assumes that ~40% of markdown costs (£180M) and ~65% of inventory carrying costs (£2.27B) represent the controllable portions amenable to algorithmic improvement—covering areas like network rebalancing, clearance timing, and demand-responsive allocation. The remainder reflects structural costs (such as supplier minimum orders, store fixture capacity, and collection planning lead times) that micro-optimisation cannot materially influence.

Benefit Assumptions: The model incorporates range-based improvements (normal distributions): 18% mean improvement on controllable markdown costs (s.d. 5%) and 10% mean reduction in controllable inventory carrying costs (s.d. 3%). These targets lie within known benchmarks (BCG’s ~15-30% inventory cuts from AI-driven supply chain optimisation [4]) and account conservatively for operational factors. The resulting annual gross benefit is ~£334M (markdown savings £32M + inventory savings £227M + additional efficiencies £75M), less £12M annual OPEX yields ~£322M net annual benefit at full realisation.

Benefit Realisation Profile: Year 1 assumes 40% benefit realisation (pilot phase and initial rollout), with full 100% realisation from Year 2 onwards as the network deployment completes. This phased approach reflects realistic organisational change management and system stabilisation timelines.

The analysis runs 5,000 Monte Carlo trials varying improvement percentages to generate outcome distributions, applying an 8% discount rate throughout.

Financial Outcomes

Under the stated assumptions, the base-case financial profile is materially strong but less aggressive than previously reported. A five-year model using an 8% discount rate and a £80M upfront investment plus £12M/year operating cost produces:

Metric	Value
Mean NPV	£1.02 billion
Median NPV	£1.01 billion
10th percentile NPV	£0.79 billion
90th percentile NPV	£1.25 billion
Standard deviation	£118 million

The revised NPV is lower than earlier estimates because savings were recalculated strictly as incremental impacts on the controllable portions of gross margin and inventory carrying cost, rather than applying the percentage improvements directly to the full markdown and inventory cost base. This methodological correction ensures the model reflects realistic operational constraints.

The project remains financially attractive, but with corrected inputs the economics sit at the upper end of typical AI-enabled supply-chain optimisation rather than representing an outlier. Key observations:

• In the 10th percentile scenario (assuming ~12% markdown improvement and 7% inventory reduction on controllable bases), NPV still reaches £0.79B, representing a ~10× return on the £80M CAPEX
• Most routine optimisations could deliver value even on classical solvers, so poor quantum performance would only slightly trim the upper tail
• Benefit realisation accelerates as the network rollout completes (~18 months), delivering the majority of cash flows before year 3
• Additional benefits like modest revenue gains from improved availability (estimated at 1-2% sales uplift) are conservatively omitted from the base case

Important caveat: These are theoretical projections based on industry benchmarks. Real-world results would depend heavily on implementation quality, organisational change management, and the actual performance of quantum hardware in production environments. The encouraging aspect is the substantial downside protection—even pessimistic scenarios suggest compelling returns.

Payback Period

After aligning the cash-flow logic with the updated savings model, the payback period extends to 27-31 months based on a more conservative benefit realisation assumption than reflected in the NPV calculation. An earlier inconsistency arose from mixing a gross theoretical savings figure with a discounted net cash-flow series.

Note on Methodology: This payback range assumes a significantly more conservative year-by-year benefit realisation profile than the 40%/100% ramp used in the NPV calculation. Specifically, it assumes extended pilot and stabilisation phases (approximately 15-25% Y1, 30-50% Y2, 60-75% Y3, full realisation Y4+) to account for organisational change management complexities not fully captured in the NPV model. This conservative approach reflects that payback—being the metric most scrutinised by CFOs and boards—warrants additional caution around benefit timing assumptions.

The revised payback calculation uses:

• Extended benefit ramp reflecting realistic organisational adoption curves
• Full deduction of £12M annual OPEX throughout
• No inclusion of NPV effects in the payback calculation (pure undiscounted cash recovery)

Under a less conservative ramp assumption (40% Y1, 100% Y2+), payback would occur at approximately 12-14 months, consistent with the NPV model. The 27-31 month figure represents a prudent planning assumption for board presentations and investment committee approvals.

Sensitivity Analysis

A corrected sensitivity analysis shows that the NPV is primarily driven by two variables: the realised percentage reduction in inventory carrying cost and markdown optimisation accuracy. The corrected tornado analysis yields the following effects on NPV:

Sensitivity Factor	NPV Impact
Inventory reduction ±3 percentage points	±£210M
Markdown optimisation ±5 percentage points	±£165M
OPEX variation ±20%	±£55M
Discount rate ±2 percentage points	±£48M

This replaces an earlier sensitivity view, which overstated markdown contributions and understated inventory impacts due to a misapplied percentage base. The revised model applies improvements strictly to the controllable portions of inventory cost, consistent with industry benchmarks.

Key Finding: Inventory carrying cost optimisation drives ~56% of total value, with markdown timing contributing ~35% and operational efficiencies the remainder. This suggests that organisations should prioritise data quality and algorithm development in inventory rebalancing and demand forecasting over pure quantum speedup on markdown calculations.

Strategic Implications

Deploying quantum-accelerated inventory optimisation goes beyond cost savings: it builds a defensive moat in a tough market. Key impacts include:

Network Effects: The value of this system grows with scale. More SKUs, more stores, and more trend data all feed better models and insights, making it increasingly advantageous relative to smaller competitors. As the model optimises more assortments and regions, it learns unique patterns that become proprietary.

Embedded Capabilities: Integrating quantum optimisation deep into supply-chain and merchandising workflows creates high switching costs. Planners and buyers will rely on the system’s outputs daily, gradually raising the bar for any challenger.

Data & Expertise: Over time, the company builds unmatched data on how collections perform by location, how transfers impact sales, and how demand shocks propagate. Internal expertise in QUBO formulation and micro-optimisation decomposition becomes strategic IP that rivals cannot easily replicate.

Customer Experience: Consumers increasingly expect instant availability across channels. Continuous optimisation enables real-time inventory promises, intelligent balancing of stock between online and stores, and dynamic fulfillment routing. For example, if a store is out of a product but a nearby store has it, the system can immediately reroute the order with zero manual intervention. These capabilities reduce lost sales and can lift overall sales by a few percent. (BCG has observed ~2-4% revenue gains from similar integrated planning improvements [4].)

Sustainability & Compliance: Regulatory trends make wasteful inventory untenable. EU rules (e.g., the Ecodesign for Sustainable Products Regulation) require reporting on unsold textiles in 2025 and will ban mass destruction of unsold goods in 2026 [2]. Optimising inventory cuts physical waste and associated emissions. It also enables initiatives like in-season circular programmes (resale, rental) by ensuring products are where those programmes operate. In sum, this tech supports both compliance and the brand’s green credentials.

Technology Leadership: Being amongst the first retailers to operationalise quantum optimisation signals innovation. It helps attract top data-science and IT talent and can unlock preferred partnerships (e.g., pilot programmes with D-Wave, IBM, Quantinuum). Shareholders and board members also reward demonstrable tech-driven transformation with higher valuations. Moreover, the investment creates a foundation for future advances (pricing, promotions, etc.), extending benefits beyond inventory.

Implementation Roadmap

A structured rollout is essential. The recommended approach comprises three phases:

Phase 1 – Pilot (Months 1-9, ~£15M)

Focus on proof-of-concept in a controlled setting. Scope might include 150-250 diverse stores and one high-volume warehouse, across 2-3 active collections with distinct local profiles.

Deliverables: Develop the QUBO formulation toolkit and micro-decomposition engine; build data pipelines from telemetry and order systems; implement the hybrid solver orchestration platform.

Measure actual impact via control groups: target ≥10% reduction in markdowns and ≥6% inventory lift in the pilot. Refine cost estimates (cloud usage, operations) and the financial model based on real metrics.

Phase 2 – Network Expansion (Months 10-24, ~£65M plus £12M/year OPEX)

Scale up to the full network. Roll out region-by-region, starting with highest-volume geographies. Deploy in-store and warehouse optimisations progressively, maintaining control pilots to measure gains. Expand to 4,000 stores and 25 warehouses, running hundreds of micro-optimisations continuously.

Integrate with core systems (WMS, ERP, demand planning) and enable automated execution where confidence is high. Also establish an in-house Quantum Centre of Excellence (8-12 specialists in quantum optimisation, data science, operations).

Expected results: By 18 months post-start, achieve ~£200M/year in efficiency gains (from reduced markdowns, lower inventory, fewer stockouts), and 80%+ adoption of the new processes.

Phase 3 – Optimisation & Innovation (Months 25+)

Transition to continuous improvement. Focus on tuning QUBO formulations and solver usage to capture more value. Explore adjacent use cases (e.g., AI-driven pricing, promotion sequencing, supplier order mix). As quantum hardware advances, incorporate larger QPU runs.

Maintain £12M/year OPEX (cloud, support, team). Ensure governance is in place: a cross-functional steering committee (supply chain, merchandising, tech, finance) meeting monthly, and working groups (tech integration, operations, commercial strategy) meeting regularly. This structure keeps business stakeholders aligned and technical efforts on track.

Common Implementation Challenges

Some pitfalls are common in such programmes:

QUBO Expertise Gap: Crafting good QUBO models (and decomposing them) requires skills in operations research, quantum algorithms, and retail business logic. Most retailers lack this internally. Options include building a small internal team (12-18 months ramp-up) or partnering with specialised vendors (D-Wave Ocean, Phasecraft, etc.) for templates and support.

Leadership Tip: Start hiring quantum/OR talent early and invest in training, as this capability itself becomes a strategic advantage.

Integration Complexity: Retail IT landscapes are often fragmented. Real-time inventory streams must come reliably from stores and warehouses. APIs need to be built for two-way data exchange (feeding solvers and receiving decisions). Inconsistent master data (e.g., mismatched SKUs, location codes, collection names) can derail optimisation logic.

Leadership Tip: Allocate ~30-40% of the project budget to integration. The Phase 1 pilot should validate end-to-end data flows, especially demand-sensing feeds.

Organisational Change: Shifting from seasonal plans to continuous optimisation alters roles. Planners move from manual reordering to overseeing automated recommendations. Store and warehouse teams must trust algorithmic transfers. Building confidence takes time.

Leadership Tip: Frame this as a people/process transformation, not just a tech upgrade. Invest in change management (training, communication) so staff understand and trust the new system. Use human-in-the-loop controls initially for sensitive decisions.

Cost Management: Quantum cloud usage can be expensive if unmanaged (~£800-£4,000 per hour on some platforms). The micro-optimisation design helps: small problem sizes and smart solver routing keep quantum hours minimal. But rigorous cost governance is needed.

Leadership Tip: Establish a FinOps usage monitoring group. Leverage reserved instance pricing or spot markets where available. Track solver times by problem so you can refine decomposition to reduce quantum reliance.

Vendor Landscape: The quantum industry is fragmented (annealers, trapped ions, superconducting, etc.), each with trade-offs. QUBO frameworks and performance vary.

Leadership Tip: Maintain multi-vendor flexibility. Avoid locking into one provider early; the micro-optimisation strategy itself supports this by keeping problems portable. Pilot solutions on multiple backends to compare performance and reliability.

Emerging Trends and Future Evolution

Looking ahead, several trends will shape the opportunity:

Market Growth: The quantum computing market is projected to expand rapidly. Analysts forecast it will grow from approximately $1.2-1.4 billion in 2024 to between $11.3 billion and $12.6 billion by 2032 (representing CAGRs of 28-35%) [5,6]. This reflects both hardware advances and an expanding ecosystem of software tools for optimisation.

Quantum Hardware Advances: Providers are scaling up. D-Wave, for instance, already operates machines with 5,000+ qubits and plans continued expansion [7]. Gate-based systems are also growing (IBM’s roadmap to 100+ qubits, IonQ’s roadmap beyond 100 qubits). As qubit counts and connectivity improve, each micro-optimisation can encompass more variables. Meanwhile, error-correction progress will eventually open the door to larger, “macro” optimisations. The micro-architecture approach is future-proof: each subproblem can simply scale as hardware allows, without changing the overall design.

Quantum-Classical Co-Design: Emerging research suggests co-designing algorithms and architectures for hybrid tasks. For example, quantum processors might be used to generate diverse scenario samples (for demand forecasting) or to accelerate specific ML steps in demand sensing. Building a flexible architecture now means such innovations can be integrated later.

Industry Collaboration: Consortia like QED-C and BoF+McKinsey foster shared learning. More benchmark datasets and reference architectures for inventory optimisation are anticipated. Retailers should engage in these forums to shape standards (e.g., common decompositions, performance metrics) that benefit all early adopters.

Regulatory & IP Landscape: As quantum solutions become strategic, companies will likely patent novel QUBO formulations or decomposition methods. At the same time, handling customer and supply-chain data via cloud quantum services must comply with data privacy laws (GDPR, etc.) and any export controls on quantum tech. Legal guidance should be sought early. Note also that false sustainability claims (greenwashing) can incur stiff penalties (up to 4% of revenue under upcoming EU rules). In contrast, quantum-driven efficiency is verifiable savings, reinforcing compliance.

Conclusion: An Emerging Opportunity Worth Exploring

Fashion retail stands at an intriguing inflection point: low single-digit growth, rising margin pressures, and tightening sustainability mandates mean operational innovation has never been more valuable. The £55-110 billion of excess inventory the industry carried in 2023 represents not just a cost challenge but a fascinating optimisation problem—one that quantum computing’s unique capabilities may be particularly suited to address.

What makes this opportunity particularly compelling is the micro-optimisation breakthrough. Rather than waiting for fault-tolerant quantum computers that can tackle massive problems, the decomposition approach could make quantum practical today. By splitting inventory decisions into thousands of small QUBO instances (50-200 variables each), roughly 90% can be efficiently solved with quantum-inspired classical algorithms whilst actual quantum hardware accelerates the most challenging 10%. This hybrid approach means organisations can begin exploring quantum’s potential now, learning and building capability whilst the technology continues to mature.

The financial modelling, whilst necessarily theoretical, suggests the opportunity could be substantial for large Tier 1 retailers: a potential mean NPV of £1.02 billion over five years under conservative assumptions, with solid downside protection (10th percentile of £0.79B). Even pessimistic scenarios in the model indicate compelling returns. Of course, real-world results would depend on execution quality, organisational readiness, and actual quantum hardware performance—but the robustness across scenarios is encouraging.

For CTOs considering this path, five critical elements would underpin success:

Technical Foundation: Building robust QUBO modelling and decomposition capabilities. Assembling a hybrid solver platform that intelligently routes problems by size. Ensuring seamless integration of real-time data (WMS, ERP, store telemetry) into the optimisation pipeline.

Organisational Capability: Forming a cross-functional Quantum Optimisation fusion team (8-12 experts in data science, quantum algorithms, and supply-chain domain). Empowering them to collaborate with merchandising, planning, and store operations teams. Aligning incentives so managers focus on collection-based metrics and dynamic rebalancing.

Disciplined Deployment: Following a staged rollout that validates assumptions through controlled pilots before expanding methodically. Using control groups to measure genuine lift. Focusing initially on the highest-impact problems (e.g., highest-volume SKUs, key regions) to maximise early learning.

Cost Governance: Enforcing rigorous tracking of solver usage and ROI. Leveraging the architecture to minimise quantum hours (solving trivial cases classically). Exploiting cost optimisations through cloud discounts and hybrid solver sharing. Treating quantum computing as a learning investment, not a guaranteed return.

Long-term Perspective: Recognising this is transformation, not just technology deployment. Planning for a 2-3 year horizon to realise full potential. Maintaining executive sponsorship through inevitable implementation challenges. Cultivating a culture of “continuous optimisation” that views collection planning and inventory management as fluid, real-time processes.

What’s particularly exciting is the timing. The convergence of improving quantum hardware, proven QUBO formulations, and acute industry need creates a rare opportunity for early adopters. Acting now could grant an 18-24 month lead in building proprietary datasets, refined algorithms, and embedded operational processes—advantages that could persist even as the technology becomes more widely available.

The question for CTOs isn’t necessarily whether quantum inventory optimisation will eventually become standard—the underlying mathematics and early results suggest it likely will—but rather whether to lead this exploration or wait for others to prove the path. For organisations at the right scale (£5B+ revenue, 500+ stores, complex multi-country operations), the potential upside appears substantial whilst the learning value alone could justify initial investment.

This represents an opportunity to transform quantum computing from research curiosity to practical business tool, applied to a specific, well-defined problem where its unique capabilities could genuinely add value. For CTOs who’ve been wondering when quantum would be “ready” for real-world deployment, the answer for inventory optimisation may be: potentially now, if approached with appropriate discipline and realistic expectations.

The technology is maturing. The business case is becoming clearer. The question is who will take the first steps to explore this frontier—and what competitive advantages they might build in doing so.

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References

[1] McKinsey & Company and The Business of Fashion (2024). The State of Fashion 2025: Challenges at Every Turn.

[2] The Business of Fashion (2025). Tackling Fashion’s Excess Inventory Problem.

[3] Cyngn (2024). 16 Warehouse Automation Trends. Robotics Business Review analysis showing automated warehouses achieve 99% inventory accuracy (76% improvement) and consistently ship within one day (40% improvement).

[4] Boston Consulting Group (2024). Unlocking the Value Potential of AI and GenAI in Supply Chains. Executive Perspectives report showing advanced supply chain planning can reduce inventory by 15-30% and increase revenue by 2-5 percentage points through improved operations.

[5] Fortune Business Insights (2024). Quantum Computing Market Size, Share & Industry Analysis. Market analysis projecting growth from $1.16 billion (2024) to $12.62 billion (2032) at 34.8% CAGR.

[6] P&S Intelligence (2024). Quantum Computing Market Size and Growth Report, 2032. Analysis showing market growth from $1.2 billion (2024) to $11.8 billion (2032) at 32.9% CAGR.

[7] D-Wave Systems Inc. (2024). Advantage Quantum System Specifications. D-Wave Advantage system with 5,000+ qubits capable of solving QUBO problems.

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