Quantum computing has progressed greatly, yet no current hardware platform achieves coherence preservation, precise control, and scalability at the same time. In this commentary, we consider molecular qubits as a conceptually distinct approach to quantum hardware, in which part of the required functionality can be encoded directly through chemically programmable structure rather than imposed entirely by external engineering. Owing to their atomic precision, synthetic reproducibility, tunable interactions, and diverse internal energy levels, molecular systems provide attractive opportunities for quantum information science. We review advances in molecular qubits, emphasizing their potential for uniformity, scalability, programmability, and coherence at elevated temperatures. At the same time, major challenges remain, including single-molecule readout, addressability, device integration, and the realization of high-fidelity entangling gates. We argue that the near-term impact of molecular qubits may not directly replace established platforms but instead enable hybrid architectures and promote a new paradigm of quantum hardware: the systematic chemical design of quantum function. In the long term, we may be able to address the challenges outlined in the DiVincenzo criteria and thereby enable universal quantum computation.
Quantum computing has progressed greatly, yet no current hardware platform achieves coherence preservation, precise control, and scalability at the same time. In this commentary, we consider molecular qubits as a conceptually distinct approach to quantum hardware, in which part of the required functionality can be encoded directly through chemically programmable structure rather than imposed entirely by external engineering. Owing to their atomic precision, synthetic reproducibility, tunable interactions, and diverse internal energy levels, molecular systems provide attractive opportunities for quantum information science. We review advances in molecular qubits, emphasizing their potential for uniformity, scalability, programmability, and coherence at elevated temperatures. At the same time, major challenges remain, including single-molecule readout, addressability, device integration, and the realization of high-fidelity entangling gates. We argue that the near-term impact of molecular qubits may not directly replace established platforms but instead enable hybrid architectures and promote a new paradigm of quantum hardware: the systematic chemical design of quantum function. In the long term, we may be able to address the challenges outlined in the DiVincenzo criteria and thereby enable universal quantum computation.
We review the progress made in integrating magnetic molecules containing transition metal ions into nanoelectronic solid-state devices and using them as molecular spin qubits. Molecular spin qubits offer numerous advantages over other material platforms, such as being inherently quantum systems with discrete energy levels, having a rich variety of possible chemical designs, and being synthesizable in large quantities with atomic precision. However, the integration of magnetic molecules into practical and scalable molecular spin qubit devices (MSQDs) that offer initialization, coherent control, and readout of quantum spin states remains one of the major bottlenecks on the way toward applications of these systems in quantum computing technologies. We discuss the evolution of the field of MSQDs and review the first successful implementation of essential qubit operations in a single-molecule transistor based on a TbPc 2 molecule. Further development has been achieved through the merging of molecular electronics with the circuit quantum electrodynamics (cQED) approach where molecular spin states can be manipulated by the microwave frequency photons confined in a superconducting waveguide resonator. We discuss examples of such hybrid devices operating via coupling of a superconducting resonator to large spin ensembles, such as bulk samples and thin films. We also review a possible architecture for single-molecule cQED devices and the potential advantages that it offers. Then, we highlight the possibility and benefits of the optical initialization and readout of molecular spin states. This approach has been realized by using an optical pumping technique for the initialization and changes in photoluminescence for the readout of large molecular spin ensembles coupled to a superconducting resonator. The field of MSQDs is highly interdisciplinary, bringing together progress in synthetic chemistry, molecular electronics, cQED, and optical measurements. While it is still in its infancy, there are promising theoretical designs and encouraging proof-of-concept experiments, and we discuss several proposed device architectures that are eagerly awaiting experimental realization. Advances in hybrid MSQDs will open horizons for quantum technologies.
Efforts to harness quantum hardware relying on quantum mechanical principles have been steadily progressing. The search for novel material platforms that could spur the progress by providing new functionalities for solving the outstanding technological problems is however still active. Any physical property presenting two distinct energy states that can be found in a long-lived superposition state can serve as a quantum bit (qubit), the basic information processing unit in quantum technologies. Molecular systems that can feature electron and/or nuclear spin states together with optical transitions are one of the material platforms that can serve as optically addressable qubits. The attractiveness of molecular systems for quantum technologies relies on the fact that molecular structures of atomically defined nature can be obtained in endless diversity of chemical compositions. Crucially, by harnessing the molecular design protocols, the optical and spin (electronic and nuclear) properties of molecules can be tailored, aiding the design of optically addressable spin qubits and quantum sensors. In this contribution, we present a concise and collective discussion of optically addressable spin-bearing molecules - namely, organic molecules, transition metal (TM) and rare-earth ion (REI) complexes - and highlight recent results such as chemical tuning of optical and electron spin quantum coherence, optical spin initialization and readout, intramolecular quantum teleportation, optical coherent storage, and photonic-enhanced optical addressing. We envision that optically addressable spin-carrying molecules could become a scalable building block of quantum hardware for applications in the fields of quantum sensing, quantum communication and quantum computing.
Quantum computation has emerged as a prominent research field with the potential to revolutionize information processing. Recent advancements have focused on extending qubit coherence times and enhancing error correction and fault-tolerant protocols, achieving coherence time to gate time ratios as high as 10⁸ and reducing error rates to as low as 0.143% ± 0.003% per cycle for 101 qubit system. The next critical challenge lies in scaling up qubit systems to support large-scale quantum computation that exceeds the performance of classical supercomputer. In this study, we summarize the working mechanisms, physical properties, gate operations, and limitations of three leading qubit platforms—superconducting qubits, trapped-ion qubits, and neutral atom qubits. Through a systematic comparison, we identify the key challenges that hinder qubit system scalability. Finally, we outline future research directions to address these challenges, including exploring the fundamental physics underpinning long coherence times, improving control tools such as laser systems, enhancing material quality and fabrication precision, and investigating hybrid quantum architectures. We hope this study offers a valuable perspective for researchers working toward the development of scalable and reliable quantum computing systems.
The predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic correlation, excited states, and complex energy landscapes. Quantum computing offers a fundamentally different computational paradigm in which quantum states are encoded and manipulated directly rather than approximated on classical hardware. Here we discuss where this approach may provide a genuine scientific advantage in chemistry, materials science, and biochemistry. Promising directions include the high-accuracy treatment of correlated active spaces, improved excited-state simulations, and accelerated exploration of combinatorial structure spaces. The central challenge is therefore not qubit scaling alone, but demonstrably chemically meaningful gains in predictive reliability. We argue that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods. Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects, and claims of usefulness must be tied to the specific regime being discussed. Quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.
Accurate prediction of molecular structures and properties is vital for chemistry; materials science, and drug discovery, yet classical electronic-structure methods often fail for strongly correlated systems, large basis sets, and complex potential-energy landscapes. Quantum technology encompassing quantum computing, quantum machine learning, and hybrid quantum classical strategies offers a fundamentally new paradigm by encoding many-electron wave functions directly on qubits and exploiting superposition and entanglement to explore exponentially large Hilbert spaces. This review synthesizes recent algorithmic and hardware advances relevant to molecular modelling, including the Variational Quantum Eigensolver (VQE), Quantum Phase Estimation (QPE), quantum unitary coupled-cluster (q-UCC) families, equation-of-motion and subspace methods for excited states, tensor-network hybrids, and quantum kernel and variational QML approaches. We examine noise-aware hybrid workflows, error-mitigation techniques, symmetry-preserving ansätze, and operator-factorization methods that reduce measurement and gate overhead. Representative applications are discussed: ground- and excited-state energy prediction, potential-energy surface mapping, geometry and transition-state optimization, spectroscopic property estimation (IR, UV–Vis, NMR, EPR), and reaction-dynamics scenarios where non-adiabatic effects and conical intersections dominate. Resource estimates and scaling analyses clarify current NISQ limitations qubit counts, circuit depth, shot complexity and delineate the roadmap to fault-tolerant QPE for chemical accuracy. We compare quantum approaches with classical baselines (DFT, CCSD (T), multireference methods), identifying domains where quantum methods already show promise (strong correlation, multi-reference dissociation, spin-state ordering) and where classical methods remain competitive. Finally, we highlight near-term industrial opportunities in drug design, catalysis, CO2 capture, and energy materials, and outline critical research directions: algorithmic reductions in measurement/precision cost, hardware improvements in fidelity and connectivity, scalable ansatz design, and integrated software stacks for reproducible hybrid simulations. Together, these developments indicate that while practical, large-scale quantum advantage for general chemistry remains future work, quantum technologies are rapidly maturing into powerful tools for targeted molecular problems that are intractable with existing classical techniques.
Quantum chips form the foundational hardware for quantum computing, promising transformative impacts across optimization, secure communications, and artificial intelligence. This paper surveys five leading qubit implementations—superconducting circuits, neutral-atom arrays, trapped ions, photonic systems, and silicon-based quantum dots—highlighting their distinct operating principles, recent milestones, and inherent trade-offs. Superconducting platforms leverage mature microfabrication to achieve hundreds to thousands of qubits with rapid gate times but require millikelvin environments and face millisecond-scale coherence limits. Neutral-atom and ion-trap architectures offer exceptionally long coherence (seconds to hours) and high-fidelity operations, making them ideal for quantum memory and precision simulation, yet scaling remains constrained by control complexity. Photonic chips excel at room-temperature operation and quantum networking via entangled photons, though deterministic two-qubit gates and on-chip integration pose challenges. Silicon quantum dots promise CMOS compatibility and sub-microsecond spin coherence, but demand atomic-scale fabrication precision and advanced cryogenic control. We analyze the tension between qubit count, error rates, and engineering practicality, and discuss emerging solutions such as hybrid integration, advanced error-correction codes, and novel materials. Looking ahead, coordinated optimization across these platforms—combining fast superconducting processors, long-lived atomic memories, photonic interconnects, and scalable silicon arrays—will be critical to transition quantum chips from research prototypes to industrially viable systems that redefine computing, communications, and AI.