Quantum-Enhanced Beamforming Boosts 6G Sensing and Communication Simultaneously

The next generation of wireless networks is being asked to do two jobs at once: carry enormous volumes of data and act as a radar that senses the world around it. This dual mission, known as integrated sensing and communications (ISAC), has become one of the defining design challenges for sixth generation (6G) systems. A new study published in Mobile Networks and Applications tackles a central piece of that challenge—how to steer radio energy intelligently so that a single transmitter can simultaneously serve multiple users with high-quality communication links and precisely locate targets in its environment. The research team, led by Yongliang Sun of Nanjing Tech University together with colleagues at the Harbin Institute of Technology (Weihai), Zhejiang University of Technology, and Simon Fraser University, proposes a beamforming framework that combines a clever optimization strategy for multi-layer metasurface hardware with a quantum-inspired reinforcement learning algorithm.

At the heart of the work is a relatively new hardware concept called the stacked intelligent metasurface (SIM). Unlike conventional massive antenna arrays, an SIM consists of several layers of nearly passive metamaterial elements stacked in front of the transmitter. Each layer can shift the phase of radio waves passing through it, and by coordinating the phase shifts across all layers, the stack can perform part of the beamforming task directly in the electromagnetic wave domain—before the signal even reaches the digital baseband. This wave-domain processing offers the prospect of enormous antenna apertures and fine-grained spatial control at far lower cost and power consumption than fully digital arrays, which is precisely why SIMs have attracted intense attention for 6G applications ranging from holographic multiple-input multiple-output (MIMO) communications to ISAC.

The difficulty is coordination. With many layers and thousands of reconfigurable elements, jointly optimizing the phase configurations is a formidable problem. The researchers address it with a scheme they call layer contribution sorting multi-layer alternating optimization (LCS-MAO). In a naive alternating optimization, the algorithm would tune each SIM layer in a fixed sequence, treating every layer as equally important. LCS-MAO instead evaluates, at each round, how much each layer actually contributes to improving the objective, and prioritizes the layers with the greatest remaining optimization potential. Layers that promise larger improvements are optimized first and more intensively, while layers whose adjustments yield diminishing returns are deprioritized. This dynamic sorting accelerates the search for good phase configurations across the entire stack and makes the multi-layer design far more computationally efficient.

How do the authors judge whether the sensing half of the dual-function system is performing well? They adopt a power-based sensing loss metric, defined as the reciprocal of the effective sensing signal-to-noise ratio (SNR). Minimizing this quantity is equivalent to maximizing the effective sensing SNR, which in turn means sharper, more reliable estimation of target directions. This metric neatly captures the trade-off at the core of ISAC design: every watt of transmit power and every degree of spatial freedom devoted to communication beams is power and freedom unavailable for illuminating and sensing targets. A well-designed beamformer must thread the needle, concentrating energy toward users for data delivery while still sculpting enough of the transmitted waveform toward target directions that the reflected echoes can be read clearly.

On the active side of the system—the transmit beamforming computed digitally—the team turns to reinforcement learning, but with a quantum twist. Their approach builds on the soft actor-critic (SAC) family of algorithms, a widely used off-policy deep reinforcement learning method prized for its stability and its ability to learn continuous control policies. In the proposed quantum soft actor-critic (QSAC), the actor remains a classical neural network that outputs continuous beamforming actions, but the critics are replaced by variational quantum circuits. These parameterized quantum circuits process encoded information through sequences of quantum gates whose parameters are learned during training, and their measurement outputs supply the value estimates that guide the actor’s learning.

The motivation for going quantum is parameter efficiency. Each variational quantum circuit-based critic requires far fewer trainable parameters than an equivalent classical critic network, because the expressive power of a quantum circuit does not scale linearly with the number of its adjustable parameters. Fewer parameters mean less memory, potentially faster training iterations, and a reduced risk of overfitting—advantages that matter when the learning agent must adapt beamforming policies in systems with high-dimensional configuration spaces, exactly the regime that multi-user SIM-assisted ISAC inhabits. The authors report that QSAC achieves better parameter efficiency compared with a classical critic network while maintaining the quality of the learned policies.

The full framework therefore couples the two halves elegantly: LCS-MAO handles the passive beamforming across the SIM layers by sorting layers according to their optimization contribution, while QSAC learns the active transmit beamforming policies that determine how power and spatial structure are allocated among communication and sensing objectives. Because the passive and active designs interact—each layer’s phase profile changes the effective channel that the transmit beams must navigate—the joint framework iterates between them, and the combined LCS-MAO plus QSAC approach is what delivers the study’s headline results.

Those results, demonstrated through simulations of a multi-user downlink ISAC system, show that the proposed scheme converges faster and achieves lower sensing loss than the benchmark schemes considered in the study. Importantly, the sensing gains do not come at the expense of users: the framework satisfies the communication signal-to-interference-plus-noise ratio (SINR) requirements that guarantee each user’s link quality. In other words, the radar-like function of the network is sharpened without starving the data connections, which is the fundamental balancing act that any practical ISAC deployment must perform. The faster convergence is particularly significant for real-world operation, since wireless environments shift constantly and a beamforming controller that needs many training episodes to adapt would lag behind the users and targets it is meant to serve.

The broader context makes clear why this line of work matters. ISAC has moved from a conceptual proposal to a cornerstone of 6G standardization thinking, with surveys in the field describing dual-functional wireless networks that fuse radar and communication infrastructure. Reconfigurable intelligent surfaces and their stacked descendants extend this vision by giving networks programmable control over the propagation environment itself, effectively turning passive structures into tunable optical-style elements for radio waves. Prior studies have applied SIMs to multi-user beamforming in the wave domain, to sensing-communication trade-offs, and to deep reinforcement learning for sum-rate optimization; the present study adds two ingredients—a contribution-aware layer optimization that respects the layered structure of the hardware, and quantum critics that shrink the learning model without sacrificing performance.

There are, of course, caveats that temper the excitement. The results are simulation-based, and the authors note that no datasets were generated or analyzed beyond the study’s own experiments, so real-hardware validation on fabricated SIM stacks and physical quantum processing units remains future work. Variational quantum circuits today run on noisy, small-scale devices, and translating critic networks from simulation to actual quantum hardware will surface challenges that simulations cannot fully anticipate. Nevertheless, the study offers a concrete and testable recipe for one of 6G’s hardest problems: coordinating thousands of nearly passive metamaterial elements and a learning-based transmitter so that the same radio energy simultaneously streams data to users and maps the surroundings. If the promised parameter efficiency and convergence speed hold up on hardware, quantum-assisted beamforming could become a serious candidate for the intelligent surfaces that future networks drape across their base stations.

Subject of Research: Joint beamforming design for stacked intelligent metasurface-assisted integrated sensing and communications using layer contribution optimization and quantum soft actor-critic reinforcement learning

Article Title: Stacked Intelligent Metasurfaces-Assisted ISAC Beamforming Based on Layer Contribution and Quantum Soft Actor-Critic

Article References: Sun, Y., Wang, L., Meng, F., Li, B., Lu, W., & Li, C. (2026). Stacked Intelligent Metasurfaces-Assisted ISAC Beamforming Based on Layer Contribution and Quantum Soft Actor-Critic. Mobile Networks and Applications. https://doi.org/10.1007/s11036-026-02551-3

Image Credits: AI Generated

DOI: 10.1007/s11036-026-02551-3

Keywords: 6G, integrated sensing and communications, beamforming, stacked intelligent metasurfaces, quantum soft actor-critic, reinforcement learning, variational quantum circuits, alternating optimization, reconfigurable intelligent surfaces, wireless networks, signal processing, MIMO