The US finalized up to $300M in quantum awards for three architectures, targeting optics, cryogenics, control hardware, and fabrication.

NEC was one of the first developers of quantum computers back in the 1990s, but it is now among the first companies that originally researched quantum computing technologies to quit development of quantum computing hardware, according to a Diamond Online report. However, the company will continue to investigate practical applications for quantum computers.

NEC has been involved in superconducting quantum computing research since the technology's earliest days. In 1999, NEC researchers demonstrated the world's first superconducting solid-state qubit, which became a foundation of superconducting quantum computing. Variations of this technology are now being used by Google and IBM to build their quantum computers. However, it turns out that the Japanese technology giant quietly ended development of physical quantum computing machines in March 2026, and many of its researchers subsequently moved to rival Fujitsu.

Despite its historical role, NEC has apparently concluded that developing commercial quantum computing hardware will take too long to generate adequate returns. As a result, the company quietly discontinued development of quantum computing machines at the end of March, according to Diamond. A number of researchers involved with the program — reportedly including a key research leader — have since moved to Fujitsu, which continues to invest heavily in superconducting quantum computing hardware.

Meanwhile, NEC does not formally characterize the move as a withdrawal from quantum computing. In response to Diamond, the company declined to comment on whether it had exited quantum computer development and said that it continues to evaluate practical applications and industrialization of quantum technologies as well as conduct proof-of-concept projects with customers. Therefore, the move appears to represent an exit from development of actual quantum computers rather than from quantum technology altogether. NEC can continue working on quantum applications, software, algorithms, and customer projects without assuming the cost and risks associated with designing and building quantum processors and complete quantum computing systems.

For Fujitsu, the arrival of experienced NEC researchers could strengthen an already advanced superconducting quantum computing operation. Last year Fujitsu and Riken launched a 256-qubit superconducting quantum computer and have been working toward a machine with more than 1,000 qubits in 2026. In addition, Fujitsu has outlined plans to develop systems exceeding 10,000 superconducting qubits by fiscal 2030.

NEC's withdrawal is particularly notable because virtually none of the major corporate laboratories that pioneered quantum computing in the 1990s and remain intact today have completely abandoned quantum-computer hardware. IBM, the other major corporate participant in some of the earliest experimental quantum computers, has instead become one of the industry’s largest developers of quantum computing systems.

NEC is no stranger to leaving businesses that have significant prospects ahead but require massive capital investments immediately. In 1990, NEC ranked No. 1 globally in semiconductor sales, ahead of Toshiba, Hitachi, Motorola, Intel, and Fujitsu. Intel subsequently overtook NEC during the 1990s as the PC microprocessor market exploded, but in 1990, NEC was significantly ahead. So, NEC's 1999 superconducting-qubit breakthrough did not come out of nowhere, but from a company with a huge semiconductor R&D organization and deep expertise in solid-state devices. By then, NEC was no longer the No. 1 semiconductor supplier, but it remained one of the world's major semiconductor companies.

The demise of NEC's semiconductor business was not sudden. NEC dismantled its semiconductor business in stages over roughly a decade. NEC effectively gave up doing DRAM alone in 1999, when it spun off its DRAM operations into NEC-Hitachi Memory, which was named Elpida. In November 2002, NEC spun its remaining semiconductor operation — MCUs, system LSIs/SoCs and other devices — into a separate company called NEC Electronics and later renamed Renesas Electronics.

Now, NEC is reportedly stepping away from quantum computer hardware as its commercialization remains distant, but development costs continue to mount. Furthermore, given NEC's current business focus — IT services, enterprise systems, aerospace and defense, telecom infrastructure, and AI and security — quantum computers are not exactly a natural fit.

Qubits are delicate and often misbehave. A qubit’s advantage over an ordinary bit is that in addition to “0” or “1”, it can hold values of 0 and 1 at once, in a specific combination; that combination is what quantum algorithms actually run on. But any stray interaction with the surrounding world (a vibration, a stray field, a single errant photon) leaks out a bit of information about which state the qubit is in – and that’s enough to collapse the combination back into an ordinary, classical either/or state. Coupling to the environment leads to high decoherence, meaning high error rates, and is normally how we lose good quality qubits. As such, most of the engineering effort in quantum computing goes into avoiding this coupling.

Yet recently, a team at the Institute of Science and Technology Austria (ISTA), working with collaborators in Munich and Madrid, has defied this norm.

Writing in Physical Review X, Alejandro Andrés-Juanes, Johannes Fink and colleagues show that two superconducting transmon qubits, separated by a metre of coaxial cable, can settle into an entangled state by being exposed to the same quantum-correlated microwave field. Avoiding synchronized pulses, heralding, post-selection and feedback (techniques usually used to actively generate and verify entanglement, rather than to fix decoherence), the entanglement can be generated and maintained for as long as the field is on. In other words, the researchers have managed to exploit noise to generate entanglement, rather than losing their qubits to it.

This idea of entangling by using fields belongs to a family of techniques known as dissipation engineering. “The common understanding is that the environment is bad, and it will decohere your qubits,” Andrés-Juanes tells Physics World. “The main idea [here] is that one can engineer a specific interaction with the environment that is not detrimental but – like in this case – prepares your qubits in an interesting state without you having to actively apply any action on the system.”

This proposal dates back more than 20 years, to work performed by Barbara Kraus and Ignacio Cirac, but this is its first experimental realization.

The correlated environment comes from a superconducting device (a Josephson parametric converter) that splits each pump photon into a pair of photons at gigahertz frequencies. The two photons of this pair are entangled with one another and each is sent down its own coaxial cable to one of the qubits, half a metre away in either direction.

Because the noise reaching the two qubits is correlated, relaxing into it does not scramble them (specifically, it does not wash out the fixed phase relationship between them, as independent noise on each qubit would). They settle instead into a superposition, for which absorbing a photon from the field and emitting one into it interfere destructively: “the destructive interference between a photon emission event in the first waveguide and a photon absorption process at the location of the second qubit,” as the authors explain.

Once there, the qubits stop evolving and the field passes through unchanged: the entangled state is dark, invisible to the very field that created it. What the experiment does, in effect, is convert the continuous-variable (CV) entanglement carried by the two microwave beams into ordinary entanglement between two conventional transmon qubits – “we obtain qubit entanglement from CV entanglement,” Andrés-Juanes confirms – with roughly a tenth of it inherited.

Most entanglement-distribution schemes deliver entanglement as events: a pulse sequence runs, a detector clicks, a pair is announced, and the state then decays until the sequence runs again. The author’s autonomous version could enable the elimination of this machinery.

“It removes a lot of overhead on the pulse sequences you need to run to get the entanglement,” says Andrés-Juanes, an advantage that’s expected to matter most at scale, since one correlated photon source can drive many pairs at once. “Another differential feature of this protocol is that the entanglement is ‘always on’ for when you need to use it. In active protocols, you would have to reinitialize the entangled state because it would decohere after a while.”

The practical limit of this setup is temperature: “We use microwave frequencies, which are only quantum at very low temperatures,” Andrés-Juanes explains. “The real limit in the separation is how big your dilution refrigerator is”, with the longest such link so far being 30 m, at ETH Zurich. He positions the work as an intermediate-distance technology rather than a long-distance one. At optical frequencies, the constraint would lift, though achieving the same strong qubit–waveguide coupling in an atomic system would be its own problem.

For now, the numbers are modest: two qubits, entangled with a concurrence of just 0.10 (on a scale where 1 is a perfect pair), inheriting only about a tenth of the entanglement carried by the microwave beams. The group is working on a module with more than one qubit per node.

But critically, the idea is no longer theoretical. The team has now realised an entangled link that is always “there”, bringing us one step closer to distributed quantum computing (linking separate processors into one machine via entanglement), networked sensing (detectors sharing entanglement to jointly see what none could alone) and quantum repeaters (relay stations for long-distance quantum links, needed because a quantum state can’t be copied and amplified like a classical signal).

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Researchers at the Flatiron Institute in New York City have used a new tensor network (TN) scheme to simulate how Ising spin glasses evolve in time. In many cases, their classical method is more accurate than the latest quantum annealers running the same problem. The results scale in two and three dimensions and raise the bar in the ongoing competition between classical and quantum computers for simulating many-particle quantum systems.

Classical versus quantum

Quantum many-particle systems provide a good way of comparing computational techniques because their complexity grows exponentially with the number of particles. In theory, quantum computers hold an advantage rooted in entanglement and superposition. In a classical computer, the spin of a particle (up or down) is encoded in bit that is either zero (down) or one (up), but never both. In a quantum computer, the spin can be encoded in a qubit holding a quantum superposition of up and down. This is a much more natural way to represent what the simulated system is actually doing.

Recently, a team of quantum computing researchers used the D-Wave’s Advantage2 quantum annealer to simulate Ising spin glass dynamics and claimed that classical computers could not match their results. But now, Joseph Tindall and colleagues at the Flatiron Institute have shown that a classical scheme can do just as well -and sometimes better- than the annealer.

In this occasion, the competition involved the simulation of the Ising spin glass model, which describes spins on a lattice pointing in random directions. This disordered state is product of different nearest-neighbor interactions for different pairs of spins. The difficulty of this famous problem grows with the system size, which makes it a good benchmark for comparing classical and quantum performance.

Tensor networks

The tensors used by Tindall and colleagues can be thought of as LEGO pieces. Just as LEGO bricks have knobs and tubes that snap together to build a bigger array, tensors have legs (bond indices) that join through a process called contraction to form networks. In a many-particle simulation, each tensor represents a site along with its physical attributes: for instance, the type of particle it hosts, which could be a spin or an electron. Connected together, the tensors form a network that encodes how those particles interact and how entangled they are. Widening the legs (increasing the bond dimension in technical terms), allows the network to capture the correlations more accurately.

In simple terms, the pattern of connections describes how particles can move or interact, and what Tindall and colleagues built is a tensor network representation of both the Ising spin glass Hamiltonian and wave function. The first specifies the interactions between spins and the second describes the state of spins. Writing quantum states this way has proved enormously useful for accessing large systems, where the sheer number of particles rules out any exact solution.

Tindall’s group used these tensor network ideas to simulate the spin glass model’s dynamics. But there is a catch: as a TN wave function evolves forward in time, entanglement builds up between different parts of the system and the bond indices must widen to keep track of the growing correlations. This increases the computational cost of finding the ground state of the system at different times, making it more difficult and sometimes impossible.

Belief propagation

To compensate for this, the team used a belief propagation (BP) message passing approach to perform the contraction. In this scheme, each tensor receives a compact summary of what the rest of the network “looks like” from its perspective (the effective environment of each tensor). This unlike the conventional approach of accounting for every single contribution exactly. As Tindall points out, their approach can be understood as a mean field approximation on each tensor’s environment since the pieces of information reaching a particular given tensor from each of its neighbours are assumed to be independent.

By implementing this TN–BP approach, their classical implementation could evolve the system to much longer times than conventional TN schemes and manage far enough to reach the regime the quantum annealer operates. Conceptually, passing messages about an effective environment lets the computation to keep pace with the entanglement inherent to the system’s time evolution. The researchers then measured how the spin at one site relates to the spin elsewhere on the lattice (the two-point correlator) at various times and system sizes on two and three-dimensional lattices with cylindrical, diamond and cubic geometries.

Starting with a cylindrical lattice, the team compared the error in the two-point correlator error from the annealer with that from the TN-BP message passing approach. Once the bond dimension was large enough, the classical error was markedly lower error than the quantum counterpart at different annealing. On the diamond lattice, the classical error obtained again came in below the annealer’s, while on the cubic lattice the error was of the same order at a fixed annealing time.

What comes next

This work, which is described in Science, shows how fast classical simulation of quantum physics is advancing and how the on-going competition between classical and quantum computation is driving this progress.  In the future, Tindall and colleagues want to apply the TB-BP to interacting electronic systems such the Hubbard model. Additionally, the group is also extending their scheme to finite temperature problems as well as maintaining their open-source tensor network quantum simulator library.

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Building a useful quantum computer is not simply a matter of adding more qubits. The greater challenge is making these qubits reliable enough to perform long computations without errors overwhelming the result. Quantum error correction addresses this problem by encoding each logical qubit across many physical ones, but it comes at a cost: many of the operations required for a general-purpose or “universal” quantum computer become highly resource intensive once fault-tolerant error correction is introduced.

To address this resource challenge, researchers at the University of California, Davis, US have developed a classical simulation method that efficiently models the preparation of some of the most demanding quantum states. The method, which they describe in PRX Quantum, works even for large, high-fidelity protocols that were previously beyond reach.

Building a universal quantum computer

Logical operations in fault-tolerant (that is, error-corrected) quantum computing architectures fall into two broad categories. The first category is a set of operations known as Clifford gates that are relatively straightforward to implement and, importantly, can be simulated efficiently on a classical computer. By themselves, however, Clifford gates are not computationally universal. For that, you also need non-Clifford operations, which lie outside the set of classically-simulable gates and provide the missing ingredient for universal quantum computation.

To realize these non-Clifford operations in a fault-tolerant way, some qubits need to be in a special state known as a magic state. Preparing these magic states with sufficiently high fidelity is expected to dominate the cost of large-scale error-corrected quantum computers, so quantum computing theorists are searching intensively for more efficient preparation protocols. The problem is that this search contains a challenge of its own: how can we compare the efficiency of these approaches?

To assess a magic-state preparation protocol, we need to simulate it under realistic levels of error-inducing circuit-level noise. Yet the same non-Clifford operations that make magic states indispensable for quantum computation also make them difficult to simulate. Existing methods therefore become prohibitively expensive as protocols grow, limiting exact simulations to relatively small logical circuits.

Simplifying a difficult simulation

Rather than searching for a more efficient simulation algorithm directly, the UC Davis team of Samyak Surti, Lucas Daguerre and Isaac Kim first asked a more fundamental question: what mathematical structure do these protocols share?

Their framework for answering this question encompasses three broad classes of logical magic-state preparation protocols: code switching, magic state distillation, and Pauli-square-root Clifford (PSC) measurement-based protocols. In the first two classes, error propagation is relatively straightforward to analyse, but PSC protocols require a more sophisticated mathematical treatment.  Rather than viewing these protocols simply as quantum circuits, Surti, Daguerre and Kim characterized their underlying algebraic structure, showing that Pauli errors (the fundamental types of qubit errors) propagate in a highly constrained and predictable way under sequential commutation. This commutation preserves the algebraic relationships between errors and logical operators, while anti-commuting operations exhibit predictable transformations rather than generating uncontrolled complexity.

These properties mean that commuting operations can be systematically reordered without changing the outcome, allowing much of the circuit’s complexity to be absorbed into its algebraic structure. Hence, rather than tracking an exponentially large quantum state, the simulator follows how a compact description of logical Pauli and Clifford errors evolves through the protocol.

The paper formalizes these ideas through a sequence of lemmas, propositions and theorems that establish the mathematical properties of PSC protocols. The result is a series of algorithms for simulating realistic, noisy, logical magic-state preparation protocols with a computational cost that scales polynomially with both the number of qubits and what is termed the stabilizer rank of the target magic state, which is a measure of its non-Clifford complexity. Since the standard single-qubit magic state has a stabilizer rank of only two, this complexity remains manageable even as the underlying error-correcting code grows, in marked contrast to state vector simulations that scale exponentially with qubit number. The team’s advance therefore makes large-scale logical simulations practical for the first time.

Accelerating fault-tolerant quantum computing

Although the new framework does not reduce the physical resources required to prepare logical magic states, it does change how those protocols can be analysed and designed. By exposing the algebraic structure underlying a broad class of logical magic-state preparation schemes, the UC Davis team transformed a computationally hard problem into one that admits efficient classical simulation. Researchers can therefore evaluate, compare and refine candidate preparation protocols under realistic circuit-level noise without resorting to exponentially expensive simulations or uncontrolled approximations.

“The main motivation behind our work was to speed up the development of fault-tolerant quantum computer,” Kim tells Physics World. “I believe there is a large amount of uncertainty in how we will design and optimize magic state factories, which will likely remain as a bottleneck in the foreseeable future.”

As quantum computing progresses from proof-of-principle demonstrations to large-scale fault-tolerant architectures, the ability to characterize and benchmark logical operations efficiently will become increasingly important. This work offers more than just a faster simulator; instead, it provides a new theoretical foundation for designing one of the most resource-intensive building blocks of future quantum computers. “My hope is that this line of work can help us better design the fault-tolerant quantum computers we will be getting over the next few years,” Kim says.

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