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.

That gap undermines American extended deterrence because Washington cannot reassure allies or deter aggressors without accounting for South Asia’s threat calculus. AI arms developments in this region stem from colonial legacies and mistrust of great power intentions, creating a volatile strategic environment.

India’s Governance Innovation in Defense AI

India’s governance model integrates civilian oversight with defense research and ensures ethical deployment of AI. The Responsible AI Certification Pilot evaluated algorithms for explainability before clearance. Its National Strategy for AI mandates ethical review boards for dual-use systems. Developers must document bias-mitigation measures and escalation pathways. Embedding accountability at design phase stabilizes deterrence signals by reducing inadvertent algorithmic behaviors.

The Evaluating Trustworthy AI (ETAI) Framework advances defense AI governance. It enforces five principles: reliability, security, transparency, fairness, privacy, and sets rigorous criteria for system assessment. Chief of Defense, Staff General Anil Chauhan, stressed resilience against adversarial attacks, highlighting the challenge of balancing effectiveness and safety. By mandating continuous validation against evolving threat scenarios, ETAI prevents mission creep and maintains operational integrity under stress.

India’s dual use by design philosophy embeds safeguards within prototypes from inception. This contrasts with reactive models that regulate AI after deployment. Civilian launch-authorization channels separate political intent from technical execution, ensuring decisions remain under human control and reinforcing credibility in crisis moments. Regular red-team exercises involving independent experts further validate system robustness and reduce risks of false positives in autonomous targeting.

Strengthening Extended Deterrence through Cooperation

US-India collaboration on AI verification can reinforce extended deterrence by aligning technical standards and testing protocols. The iCET fact sheet outlines secure information sharing and joint safety trials. Launched in January 2023, iCET has already enabled co-production of jet engines and transfer of advanced drone technologies. Building on this foundation, specialized working groups could develop common benchmarks for adversarial-resistance testing and automated anomaly detection.

A Center for Strategic and International Studies report recommends a trilateral verification cell blending American evaluation tools with India’s ethical reviews. Joint trials of autonomous air-defense algorithms would demonstrate interoperability and resolve. A shared “AI Red Flag” system would alert capitals to anomalous behaviors and reduce strategic surprise. Embedding cryptographically secure logging of decision path data ensures an immutable audit trail for post-event analysis and confidence building.

The INDUS-X initiative, launched during Prime Minister Narendra Modi’s 2023 US visit, integrates responsible AI principles into defense innovation. By aligning standards, both countries ensure AI systems enhance strategic stability rather than undermine it. Expanding INDUS-X to include scenario-based wargaming with allied partners can stress-test ethical frameworks and calibrate thresholds for human intervention under duress. This model can extend under the Quad framework, pressuring authoritarian regimes to adopt transparency measures.

Institutionalizing Global AI Arms Control

A formal arms control dialogue should adopt India’s baseline standards for ethical AI governance. The UNIDIR report calls for universal bias audits and incident-reporting obligations to prevent unintended escalation. Carnegie scholars propose a tiered certification process under a new protocol for autonomous systems within the Convention on Certain Conventional Weapons, requiring peer review of algorithms before deployment. Embedding such certification in national export-control regimes would create global incentives for adherence.

The UN General Assembly has established an Independent AI Scientific Panel and a Global Dialogue on AI Governance to issue annual assessments on risks and norms. This mechanism can evaluate military AI applications and recommend confidence-building measures. Procedural transparency would coexist with confidentiality requirements, balancing security with mutual reassurance. Regular joint workshops on risk-assessment methodologies can diffuse best practices and diffuse mistrust among major powers.

Regional Applications and Future Prospects

India’s responsible AI framework must inspire regional adoption and confidence-building measures. Pakistan and China should engage transparency initiatives to prevent dangerous asymmetries in AI capabilities. Proposed measures include joint research on AI safety, shared performance databases, and collaborative development of detection algorithms.

Successful tests of India’s hypersonic ET-LDHCM system, capable of Mach 8 and a 1,500-kilometer range, underscore the urgency of governance frameworks before fully autonomous weapons deploy. The Quad’s model of Indo-Pacific cooperation provides a template for multilateral norms on responsible AI in defense. Extending these norms to confidence-building measures such as pre-deployment notifications and automated backchannels can reduce the risk of inadvertent escalation.

Looking ahead to the United Nations General Assembly meeting on AI governance in September 2024, American policymakers can leverage India’s experience. Joint verification exercises and an ethical audit regime will establish global norms for military AI. Integrating lessons from ETAI and iCET into the assembly’s resolutions can produce enforceable standards that bind both democratic and authoritarian states. This approach will reaffirm American extended deterrence and help prevent destabilizing AI-driven arms races worldwide.

By demonstrating that ethical AI development strengthens rather than weakens deterrence credibility, India’s model provides both technical solutions and normative frameworks for managing the military applications of artificial intelligence. Sustained international cooperation on these principles is pivotal for securing strategic stability in a rapidly evolving technological landscape.

Vaibhav Chhimpa is a researcher who previously worked with the Department of Science & Technology (DST), India. Views expressed are the Author’s own.

The Artificial Intelligence (AI) Arms Race in South Asia was originally published on Global Security Review.