Trading may look seamless from the outside, but behind every large order lies a hidden struggle with costs that go far beyond brokerage fees. Slippage, liquidity shortfalls, and market impact quietly eat into returns, often more than most investors realize.
From my experience, these aren’t just minor inefficiencies; they’re systemic challenges. And even with today’s advanced execution platforms and real-time analytics, the friction hasn’t gone away. What’s changing is how we approach it. A hybrid Quantum-AI framework offers a chance not just to speed things up, but to rethink how execution itself is optimized.
The industry measures this challenge through a metric called implementation shortfall, the gap between the price at the moment a trade is decided and the price when it’s executed. It combines the visible costs, like fees and spreads, with the hidden ones: slippage, liquidity issues, and market impact.
Earlier models, such as the Almgren–Chriss framework, gave us a foundation for understanding these dynamics, balancing volatility risk against market impact. But markets today are far more complex, with large trades often rippling through prices in unpredictable ways. That makes the question less about whether execution costs exist (they always do) and more about how well we can measure, anticipate, and reduce them.
Slippage, Liquidity, and Market Impact
Trading costs fall into two categories. The first are explicit costs, the ones you can see, like commissions, taxes, or the bid–ask spread. The second are implicit costs, which are harder to predict, such as slippage, liquidity shortfalls, and market impact. These hidden factors often turn out to be the real driver of expenses.
Slippage is often described as the surprise, the gap between the price you hoped for and the one you get once the order goes through. It usually shows up in volatile markets or when liquidity is thin. Market impact, by contrast, is the footprint left behind by a big trade. Large enough orders don’t just get filled; they shift prices as the market reacts.
Research in algorithmic trading and portfolio management has shown what I’ve observed firsthand: execution costs rise with order size, participation rate, and volatility. One of the most important findings is that market impact doesn’t increase in a straight line. Instead, it follows what’s called the square-root law; as trades grow, costs climb disproportionately. This is why seasoned traders break up large orders into smaller ones, reducing exposure while managing risk.
The formulas behind these dynamics can be complex, but the idea is simple. Trade too quickly and you risk pushing the market against yourself; wait too long and volatility might wipe out the advantage. Every execution strategy is about walking that fine line.
The Rise of AI in Trading
In working on building data platforms for trading desks, I’ve seen the limits of traditional econometric models. They often look solid in theory, but fall short when markets move at high speed. Artificial intelligence, by contrast, adapts in real time, learning directly from the complexity as it unfolds.
That’s why AI has become central to modern execution. Its biggest strength is turning massive datasets into actionable signal patterns that can guide traders through volatility while executing at speed. But I’ve also seen the challenges firsthand: these systems can feel like black boxes, powerful yet difficult to interpret or audit. And in finance, where trust and compliance are as critical as returns, that lack of transparency is never a minor issue.
Machine Learning in Execution
Supervised and reinforcement learning are proving valuable in execution, especially for optimizing order timing and size. These models adapt through trial and error, adjusting to shifting market conditions in ways static strategies can’t.
But there’s a catch. Their “black box” nature often makes decisions difficult to explain or audit, which I’ve found to be one of the biggest hurdles in getting buy-in from stakeholders.
Another challenge is overfitting, when models perform brilliantly on historical data but stumble in live markets. That’s why validation and stress testing are not add-ons, but are essentials for deploying these systems reliably at scale.
Market Microstructure Meets AI
Working with trading data has shown how AI is reshaping our understanding of market microstructure. Research and practice now demonstrate that algorithms can track order flow, fragmented liquidity, and transaction costs in real time, something older approaches struggled with.
What makes AI different is its ability to react dynamically. Instead of slicing orders mechanically, it can lean in when liquidity runs deep and pull back when volatility spikes. Done well, this kind of execution reduces slippage, leaves a smaller footprint, and manages risk with far more intelligence.
Systemic Implications
AI’s strengths come with risks. The real challenge lies in opaque models that deliver results but are difficult to audit or regulate. If too many rely on them, small shocks could escalate into broader disruptions. As the Bank for International Settlements warns, strong governance and human oversight are essential for safe adoption.
Quantum AI – The Next Frontier
Quantum’s promise is not just speed but the way it reframes financial problems. Through experience in execution research, I’ve found that challenges once modeled statistically can often be reformulated as combinatorial optimization tasks, known as Quadratic Unconstrained Binary Optimization (QUBO). In practice, this means breaking down a large order into smaller decisions and allowing quantum solvers to search for the optimal path.
This reframing makes execution less like solving calculus equations and more like navigating a vast decision map, an area where quantum devices have a natural advantage. Quantum computing has the potential to push optimization further by mapping trading and portfolio problems into structures better suited for quantum solvers.
From Classical to Quantum Models
Work in execution research shows that problems once handled with classical methods are now being tested on quantum hardware. For instance, studies using quantum annealing have solved optimal trading trajectory problems, illustrating how traditional execution challenges can be reframed for quantum systems.
Comparisons across classical solvers, digital annealers, and quantum annealers point to a clear pattern: classical approaches remain reliable for smaller problems, but as complexity increases, quantum-inspired and native methods show distinct advantages. In simple terms, the bigger the problem, the more these solvers begin to shine.
The visual compares portfolio optimization under classical and quantum solvers. Classical methods slow down as problems scale, while quantum-inspired approaches promise faster solutions and new execution paths.
State of the Field
Quantum finance today is both exciting and uncertain. Reviews highlight three areas where progress is most visible:
- Early candidates: high-value use cases such as risk analysis, derivative pricing, and fraud detection. These are data-heavy problems where classical systems often fall short.
- Incremental gains: hybrid quantum–classical approaches that boost efficiency without waiting for fully mature hardware.
- Foundational work: theoretical research, such as spin-glass optimization, that lays the groundwork for future breakthroughs.
Experience in execution research shows that while near-term devices are unlikely to disrupt markets overnight, hybrid models already offer practical benefits. Studies, including those in Nature Reviews Physics, suggest that the most immediate improvements will come from combining quantum techniques with existing AI and classical infrastructure.
This leads to a pragmatic outlook: quantum AI will not replace established methods anytime soon, but it is reframing financial problems in ways that reward early experimentation. Firms that begin integrating hybrid approaches today are likely to gain an edge in cost control, adaptability, and resilience as hardware evolves.
Bridging Architecture and Strategy
Bringing together execution strategy and emerging computational models is less about chasing hype and more about balance. On the practical side, desks still have to manage participation rates, slippage, and risk tolerance. At the same time, the choice of architecture, whether classical systems, AI-driven prediction engines, or quantum solvers, sets the limits of what those strategies can achieve at scale.
The most realistic path forward is not a sudden leap to quantum but a layered approach. Hybrid stacks that blend classical systems, AI models, and quantum-inspired solvers capture near-term efficiency while laying the groundwork for future breakthroughs.
Working with execution platforms reveals that these hybrids do more than hedge uncertainty; they build flexibility. Even the BIS points to this gradual path, with hybrid frameworks serving as the bridge until quantum hardware and algorithms fully mature.
In practice, this trajectory is already shaping what I think of as a hybrid execution stack (Visual 3). Data flows from the input layer into an AI decision engine, then through a quantum optimizer before finally routing to the execution broker. Each layer adds a safeguard, speed from AI, exploration from quantum, and reliability from classical systems working together rather than in isolation.
Regulatory and Ethical Outlook
Working with execution systems shows how quickly oversight expectations are shifting. FINRA’s guidance now emphasizes that AI tools must remain explainable and auditable, so decision trails can be reconstructed when questions arise.
Quantum adds new pressure points. Experience in the field suggests that breakthroughs may test current encryption, a risk echoed in regulatory discussions about market resilience.
The structural picture is broader still. Research from the Bank for International Settlements notes that big tech’s growing role risks concentrating power. For practitioners, this reinforces why governance models must adapt as technology evolves.
Industry Future: Human and AI in Trading
The trajectory of trading points to a blended model, not full automation. Industry research notes that the strongest applications of emerging technologies come when humans and machines work together. Traders and portfolio managers will use AI and quantum systems as copilots rather than replacements.
Hands-on work in execution shows why this matters: strategy is never just math. Objectives, risk, and outcomes demand human judgment, while AI and quantum handle the heavy lifting of data, optimization, and scale.
The future is one where technology sharpens decisions without displacing them. Human oversight remains the anchor, while AI and quantum technologies amplify speed, scale, and precision.
The Future of Execution in the Age of AI and Quantum
Execution keeps evolving, yet costs and frictions never disappear. AI has eased some of these pressures, though its opacity remains a hurdle. Quantum AI stretches the horizon further, reframing optimization itself, but faces real limits in hardware and adoption.
Working with execution research has made it clear to me that the near term belongs to hybrid systems that layer classical tools, AI, and quantum-inspired methods. They allow firms to innovate while remaining resilient, avoiding the risks of overcommitting to untested infrastructure.
Ultimately, execution will not be defined by humans or machines alone. The real progress will come from collaboration, with humans setting direction and machines amplifying speed, scale, and precision. That’s the future, I believe, is taking shape.
References:
Venturelli, D., Marchand, D.J.J., & Rojo, G. (2015). Quantum optimization of fully connected spin glasses. Physical Review X, 5(3): 031040. https://doi.org/10.1103/PhysRevX.5.031040
Almgren, R. & Chriss, N. (2001). Optimal execution of portfolio transactions. Journal of Risk, 3(2): 5–40. https://www.risk.net/journal-risk/2161150/optimal-execution-portfolio-transactions
Bank for International Settlements (BIS). (2020). Big tech in finance: Opportunities and risks. Bank for International Settlements. https://www.bis.org/publ/bppdf/bispap117.pdf
Bank for International Settlements (BIS). (2024). Intelligent financial system: How AI is transforming finance. Bank for International Settlements. https://www.bis.org/publ/work1194.pdf
Bank for International Settlements (BIS). (2024). Quantum computing and the financial system. Bank for International Settlements. https://www.bis.org/publ/bppdf/bispap149.pdf
Bender, C.M. (2019). PT Symmetry in Quantum and Classical Physics. World Scientific. https://doi.org/10.1142/q0178
Bouchaud, J.P., Farmer, J.D. & Lillo, F. (2009). How markets slowly digest changes in supply and demand. In Hens, T. & Schenk-Hoppe, K.R. (eds.). Handbook of Financial Markets: Dynamics and Evolution. Elsevier, pp. 57–160. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1266681
Deloitte. (2022). The future of trading: Human + machine collaboration. Deloitte Insights. https://www.deloitte.com/us/en/insights/topics/talent/human-machine-collaboration.html
Durin, J., Rosenbaum, M. & Szymanski, A. (2023). The two square-root laws of market impact. arXiv. https://arxiv.org/pdf/2311.18283
Financial Industry Regulatory Authority (FINRA). (2023). Artificial Intelligence in the Securities Industry. FINRA. https://www.finra.org/rules-guidance/key-topics/fintech/report/artificial-intelligence-in-the-securities-industry
Financial Industry Regulatory Authority (FINRA). (2023). Quantum Computing and the Implications for the Securities Industry. FINRA. https://www.finra.org/sites/default/files/2023-10/2023-quantum-computing-and-the-implications-for-the-securities-industry.pdf
Herman, M., McCaskey, A., Bermel, S., & Smith, C. (2023). Quantum computing for finance. Nature Reviews Physics, 5: 123–140. https://udspace.udel.edu/items/5e0d2c58-3ee1-46af-b06a-ed9c96519042
Jiang, W. (2021). Deep reinforcement learning for trading: A survey. Expert Systems with Applications, 182: 115537. https://doi.org/10.1016/j.eswa.2021.115537
Kissell, R. (2013). The Science of Algorithmic Trading and Portfolio Management. Elsevier. https://www.sciencedirect.com/book/9780124016897/the-science-of-algorithmic-trading-and-portfolio-management
Lang, B., Zielinski, M., & Feld, T. (2022). Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing. Applied Sciences, 12(9): 12288. https://research.tudelft.nl/en/publications/strategic-portfolio-optimization-using-simulated-digital-and-quan
Millea, A. (2021). Deep reinforcement learning for algorithmic trading: A review. Data, 6(11): 119. https://www.mdpi.com/2306-5729/6/11/119
Orús, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4: 100028. https://doi.org/10.1016/j.revip.2019.100028
Rosenberg, G., Haghnegahdar, P., Gogolin, C., Carleo, G., & Zoller, P. (2016). Solving the optimal trading trajectory problem using a quantum annealer. IEEE Journal of Selected Topics in Signal Processing, 10(6): 1053–1060. https://doi.org/10.1109/JSTSP.2016.2574703
Featured Image via Image by Tung Nguyen from Pixabay
