The field of materials for quantum information science is rapidly growing with a focus on scaling and integrating new solid-state qubits.However, despite the extraordinary progress, major challenges must still be overcome for scaling.Specifically, there is a critical need for efficient coupling of tens to hundreds of solid-state qubits and multiqubit error correction to mitigate environmental interactions.This Perspective looks forward to the challenges ahead in the realization of multiqubit operations and collective phenomena with a focus on solid-state quantum materials.We provide a theorists' point of view on the modeling and rational design of these multiqubit systems.Our Perspective identifies a path for bridging the gap between the model Hamiltonians used to develop quantum algorithms and control sequences and the ab initio calculations used to understand and characterize single solid-state-based qubits.
The field of materials for quantum information science is rapidly growing with a focus on scaling and integrating new solid-state qubits.However, despite the extraordinary progress, major challenges must still be overcome for scaling.Specifically, there is a critical need for efficient coupling of tens to hundreds of solid-state qubits and multiqubit error correction to mitigate environmental interactions.This Perspective looks forward to the challenges ahead in the realization of multiqubit operations and collective phenomena with a focus on solid-state quantum materials.We provide a theorists' point of view on the modeling and rational design of these multiqubit systems.Our Perspective identifies a path for bridging the gap between the model Hamiltonians used to develop quantum algorithms and control sequences and the ab initio calculations used to understand and characterize single solid-state-based qubits.
Quantum computers have gained widespread interest as they can potentially solve problems that are intractable even for today’s supercomputers, such as the simulation of quantum systems as initially proposed by Feynman. To achieve such tremendous progress, a quantum computer relies on processing the information stored in quantum bits (qubits), the fundamental units of quantum computation, similar to the bits in a classical (i.e., non-quantum) processor. In general, a quantum processor comprising several qubits is connected through a quantum-to-classical interface consisting of classical electrical circuits for manipulating and reading out their quantum state. The accuracy of an operation on a qubit is characterized using the fidelity, which is 100 % in case of a perfect operation. In practice, a fidelity between 99 % and 99.9 % is typically achieved in today’s experiments with less than 100 qubits, limited by non-idealities in the control signals and by the implementation of the physical qubit itself. Since such qubit fidelities are too low to execute relevant quantum algorithms, quantum error correction schemes have been developed which rely on a much larger number of qubits to correct for possible errors, resulting in the need for quantum processors with thousands or millions of qubits. Solid-state qubits promise the largescale integration required to scale to millions of qubits, as they exploit the lithographic fabrication techniques borrowed from the semiconductor industry. However, state-of-theart solid-state qubits, such as transmons and single-electron spin qubits, must be typically cooled to cryogenic temperatures in a dilution refrigerator to exhibit quantum behavior. Because of the limited number of qubits (< 100) in state-of-theart solid-state quantum processors, the classical controller is currently implemented by general-purpose instruments or by tailor-made controllers operating at room temperature connected via several wires to the qubits in the cryogenic chamber. However, these systems must scale to support millions of qubits. While scaling up the underlying quantum processor is already exceptionally challenging, building the electronics required to interface such a large-scale processor is just as relevant and arduous.
The ability to control the state of individual atoms is a new challenge for science and technology in the 21st century. Currently, experiments on individual quantum systems such as trapped ions, single molecules, quantum dots, superconducting qubits and photons are crucial for the rapidly growing field of quantum information processing and communication. In general, solid state systems are preferable for scaling and the choice of material plays a crucial role; for example, in classical electronic devices continual performance enhancement and miniaturization is strongly linked to the success of silicon-based technology. For quantum applications, diamond has the potential to become the material of choice, because its large bandgap enables the control of optically active impurities and higher operation temperature. This focus issue collates original research contributions from some of the leading groups in the field as a showcase for the very latest developments in diamond-based quantum technologies.
Quantum processing has the potential to transform the computing landscape by enabling efficient solutions to problems that are intractable using classical processors. The field was sparked by a suggestion from physicist Richard Feynman in 1981 that a controllable quantum system can be used to simulate other quantum systems, such as the energy band structure of complex materials or the chemical reaction rates of intricate molecules. In the 1990s, interest in quantum computing grew rapidly with the introduction of the first quantum "killer app"-the potential of a large-scale quantum processor to break certain types of public encryption schemes [1]. Recently, there has been growing consensus that myriad other fields besides data security could be impacted by the development of a quantum processor, including machine learning [2], many optimization problems [3], and Feynman's original idea of the simulation of materials properties [4]. In recent years, the field has progressed rapidly, but many technical challenges must be overcome before a large-scale quantum processor can be built. This article focuses on the development of packaging for solid-state qubits and the use of 3D integration to address this challenge.
Quantum information science is entering a second phase, the Quantum Evolution 2.0, in which the challenge has shifted from demonstrating coherent control of individual quantum states to building scalable multi-qubit processors and networks. This transition places materials science at the center of the field. Across superconducting circuits, quantum defects, quantum photonic devices, and emerging materials platforms, including two-dimensional materials and heterostructures, performance is now limited less by device design than by poorly controlled surfaces, buried interfaces, and defects whose atomic identities remain incompletely known. This review surveys the materials challenges of these quantum platforms together with the characterization methods needed to resolve them. For each platform we identify the dominant decoherence mechanisms, the current state of materials understanding, and the most pressing open materials problems. A cross-platform comparison then reveals a shared structure-coherence problem. The implicated material chemistry recurs across platforms, involving light elements in disordered or buried environments, yet no platform can quantitatively connect a specific atomic-scale structure to a measured change in coherence. We close by identifying three needs, mechanistic understanding of decoherence at the atomistic level, high-throughput proxy metrics predictive of device performance, and characterization tools built for quantum materials, whose resolution would advance coherence, scalability, and integration across all platforms.
Abstract Scalable quantum computing and communication systems strictly require the development of highly integrated quantum hardware. While prominent implementations like superconducting and semiconductor spin qubits show integration progress, their reliance on extreme cryogenic conditions raises substantial sustainability concerns, especially given global helium depletion. In contrast, solid-state quantum devices, such as nitrogen-vacancy (NV) centers, offer room-temperature operation. Although ensemble solid-state defects have been successfully utilized in quantum sensing, the realization of complex quantum registers requires highly precise arrays of single, coupled qubits. Currently, their practical integration into scalable multi-qubit arrays is fundamentally limited by the technical challenge of achieving deterministic, ultra-precise atomic injection at the angstrom scale. This Perspective examines the current state of solid-state quantum system integration, identifying recent advances and persistent bottlenecks, particularly focusing on the limitations of cooling ion sources based on Paul traps. We then propose a novel architecture combining established neutral-particle decelerator techniques with deterministic ionization to overcome these fundamental operational hurdles. This analysis aims to present a viable path for advanced integrated solid-state quantum platform development, fostering broader adoption and sustainable evolution.
Discoveries in quantum materials, which are characterized by the strongly quantum-mechanical nature of electrons and atoms, have revealed exotic properties that arise from correlations. It is the promise of quantum materials for quantum information science superimposed with the potential of new computational quantum algorithms to discover new quantum materials that inspires this Review. We anticipate that quantum materials to be discovered and developed in the next years will transform the areas of quantum information processing including communication, storage, and computing. Simultaneously, efforts toward developing new quantum algorithmic approaches for quantum simulation and advanced calculation methods for many-body quantum systems enable major advances toward functional quantum materials and their deployment. The advent of quantum computing brings new possibilities for eliminating the exponential complexity that has stymied simulation of correlated quantum systems on high-performance classical computers. Here, we review new algorithms and computational approaches to predict and understand the behavior of correlated quantum matter. The strongly interdisciplinary nature of the topics covered necessitates a common language to integrate ideas from these fields. We aim to provide this common language while weaving together fields across electronic structure theory, quantum electrodynamics, algorithm design, and open quantum systems. Our Review is timely in presenting the state-of-the-art in the field toward algorithms with nonexponential complexity for correlated quantum matter with applications in grand-challenge problems. Looking to the future, at the intersection of quantum information science and algorithms for correlated quantum matter, we envision seminal advances in predicting many-body quantum states and describing excitonic quantum matter and large-scale entangled states, a better understanding of high-temperature superconductivity, and quantifying open quantum system dynamics.