A soldier who fires on a target must be able to reason “why” afterward. Similarly, a commander who authorizes a strike must be able to justify that decision to superiors, at times to lawyers, and possibly to the public. If an algorithm makes these decisions, how is the reasoning understood when it is not understood by the engineers who built it? Judging from how military technology has advanced, this is not hypothetical. It is the situation many militaries already face as artificial intelligence (AI) takes on a growing role in identifying threats, and even recommending or engaging targets. The usual conversation about military AI focuses on speed, power, and who can compute faster or strike first. But a quieter, more important question emerges alongside it. Which systems can justify their decisions when lives, laws, and legitimacy are on the line? Quantum AI, an unlikely and still unproven contender, is being explored as a possible answer. 

What Quantum AI Actually Is 

Quantum computing provides a fundamentally different approach to processing information than classical computer methods. Rather than relying solely on classical computational methods, quantum computers exploit quantum mechanical phenomena such as superposition, entanglement, and interference to solve certain classes of problems more efficiently than conventional computers. Where Artificial intelligence is the set of processes that let computers recognize patterns and make predictions with limited human guidance. Quantum AI combines the two, using quantum computers to conduct calculations while classical AI systems manage the rest of the process. 

Militaries are interested in this combination for several reasons, including faster logistics planning and better analysis of satellite imagery. Much of the focus on quantum AI has been on speed, optimization, and solving problems that challenge classical computers. But researchers are increasingly focused on a different possibility, one that has nothing to do with how fast a system can think and everything to do with how it decides and whether one can trust its decisions. 

The Problem With Black Boxes 

Most modern AI systems, particularly the neural network models that power image recognition and predictive analysis, function as what experts call a black box. The system takes in information and produces an answer, but its internal reasoning is buried in millions of numerical values that even its designers cannot fully interpret. For everyday uses like recommending a movie or predicting traffic, this opacity is harmless. In a military context, it is not. If an AI system flags an object in satellite imagery as a probable weapon launcher or recommends that a target meets the legal criteria for engagement, someone must eventually explain the decision reasoning. This is because militaries operate under rules of engagement and international humanitarian law, both of which require distinguishing combatants from civilians and justifying the use of force. If an investigation follows a strike, whether from a military tribunal, a human rights body, or a domestic court, “the algorithm decided” may not be an acceptable answer. Accountability requires a trail of reasoning and legal justification that a human being can follow and defend. 

This is not a distant, theoretical scenario. Militaries around the world are already integrating AI into surveillance, target prioritization, and threat classification systems. As these tools take on more responsibility, the gap between what they decide and what humans can explain grows wider. That gap creates legal exposure, operational risk, and serious human accountability problems for any military that wants to claim its use of force is lawful and proportionate. 

Why Quantum AI Might Offer a Way Out 

This is where quantum AI enters the conversation, not for its speed, but potentially to provide insight into AI decision making. Some researchers argue that certain quantum AI models, particularly a type known as gate-based models, organize information in ways that more closely mirror the actual structure of the problem being solved. Rather than compressing logic into an impenetrable web of numerical weights, these models preserve the structure that allows one to trace back to a specific input or condition that led to the result. In theory, this would allow investigators to follow a clearer path from data to decision, something today’s AI engines cannot offer. 

If this promise holds up, it will matter. A military that can show step-by-step why a system chose a target is in a far stronger position to defend that decision legally and ethically. It also strengthens public trust, both domestically and internationally, at a time when the use of autonomous and AI assisted weapons is under growing scrutiny from human rights organizations and international bodies. In this framing, the race for military AI dominance is not only about who can build the fastest or most powerful system. It becomes a race to build systems that are defensible. These systems must withstand the scrutiny of a courtroom, a parliamentary inquiry, or an international tribunal. 

A Promising Idea, Not a Proven One 

It would be a mistake to overstate that this capability is currently accessible. The idea that quantum AI offers built-in decision recovery is still unproven, not an established capability. Techniques used to explain classical AI decisions, tools such as LIME and SHAP, are only beginning to be adapted for quantum models. Early results are encouraging in narrow research settings, but no military or independent certification body has yet accepted quantum-generated decision recovery as sufficient evidence in a legal or operational context. The honest way to describe this field today is as a promising hypothesis worth serious investment, not a solved problem ready for deployment. Overselling it risks the same trap that has plagued classical military AI, where systems are fielded faster than the frameworks needed to hold them accountable. 

Conclusions 

The common denominator in military technology has been about speed. It is about who can evaluate fastest, be more decisive, and often strike first. Computers systems help drive these and AI plays a key role. Quantum AI’s most valuable military application may ultimately have little to do with either. If it can genuinely offer clearer, more transparent reasoning behind life-and-death decisions, it would address one of the most serious gaps in how militaries currently use it. This deserves considerable research attention and scrutiny in equal measures.  

The technology that ultimately proves most valuable on future battlefields may not be the one that thinks the fastest. It may be the one that can provide an answer when someone finally asks why.  

Maheen Butt is a Lahore-based researcher and strategic communications professional conducting research primarily on foreign affairs. She is a Mphil graduate of International Relations from Kinnaird College for Women, and her work focuses on global affairs, emerging technologies mainly on increasing use of AI in military affairs, and defense studies. The Author’s opinions are their own.

Explainability, Not Speed, Will Define the Next Military AI Race was originally published on Global Security Review.